MAPEAMENTO TECNOLÓGICO DE PATENTES COM FOCO NA TECNOLOGIA DE CONVERSÃO DE ENERGIA TÉRMICA DOS OCEANOS
Bibliographic record
Abstract
Resumo - A crise que se instaurou no inicio da decada de 70 no seculo XX, contribuiu para que se ampliassem as discussoes em torno do aproveitamento de fontes de energias renovaveis que pudessem ser encaradas como a saida para uma eventual crise energetica. Neste estudo, buscou-se realizar um mapeamento tecnologico acerca das tecnologias utilizadas na geracao de energia a partir da conversao de energia termica dos oceanos. A pesquisa foi efetuada na base Espacenet, utilizou-se a sigla de Ocean Thermal Energy Conversion “OTEC” como argumento de pesquisa. O levantamento foi realizado no durante o mes de julho de 2018. Foram encontrados 96 pedidos de depositos de patentes entre os anos de 1976 e 2017. Os Estados Unidos foi o pais com o maior numero de depositos de patentes, seguido pelo Canada, que tambem apresentou o pesquisador individual com o maior numero de pedidos de depositos de patentes. Ambos os paises estao localizados na America do Norte e possuem grandes areas banhadas pelos Oceanos Pacifico e o Atlântico, portanto com grandes possibilidades de aproveitamento da conversao da energia termica dos oceanos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".